Q(λ)-learning fuzzy controller for the homicidal chauffeur differential game

In this paper, a Q(λ)-learning fuzzy inference system (QLFIS) is applied to a differential game. We use the homicidal chauffeur differential game as an example of the method. The suggested method allows both the evader and the pursuer to learn their optimal strategies. The parameters of the input and the fuzzy rules of a fuzzy controller are tuned autonomously using Q(λ)-learning. Simulation results demonstrate that the players are able to learn their optimal strategies.